Leakage-Controlled Machine Learning for Territorial Epidemiological Risk Stratification: A Biomedical Informatics Framework Using Administrative Health Data in the Colombian Orinoquía

Digital health observatories require predictive frameworks that transform administrative health data into reliable territorial risk indicators. We developed a biomedical informatics framework for machine-learning-based epidemiological risk stratification in the Colombian Orinoquía, with explicit attention to data-leakage prevention, validated population denominators, and unit-of-analysis discipline. From 354,088 morbidity records (2018–2023; Arauca, Casanare, Meta, Vichada) we derived 20,212 independent strata (municipality × year × diagnostic group × sex × age category × health component) and computed morbidity rates using official population projections from the Colombian National Administrative Department of Statistics (DANE), correcting a denominator instability present in the original extract. After excluding leakage-generating variables, Gradient Boosting, Random Forest, and a one-hot Logistic Regression baseline were evaluated through temporal validation, group-aware cross-validation, leave-one-department-out validation, and a geographic ablation experiment. Under leakage-controlled, stratum-level conditions, Gradient Boosting achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.913 [95% confidence interval (CI): 0.903–0.923] (Brier: 0.106). SHapley Additive exPlanations (SHAP) analysis identified diagnostic group and age category as the dominant predictors, supported by broadly stable performance without geographic identifiers (AUC 0.871) and consistent cross-department transferability (0.807–0.889); a residual municipality-level clustering effect is reported transparently. Validated denominators reversed the apparent territorial gradient, with the most remote department exhibiting the lowest documented morbidity, consistent with under-registration.